




"""simple docstring"""




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowerCAmelCase				:     Any					=				{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class        __magic_name__				(			nn.Module							):






				'''simple docstring'''





				def __init__(			self						,					_a					):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase      =   torchvision.models.resnetaaa(pretrained=_a					)
							lowerCamelCase      =   list(model.children()					)[:-2]
							lowerCamelCase      =   nn.Sequential(*_a					)
							lowerCamelCase      =   nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
							lowerCamelCase      =   self.pool(self.model(_a					)					)
							lowerCamelCase      =   torch.flatten(_a						,					start_dim=2					)
							lowerCamelCase      =   out.transpose(1						,					2					).contiguous()
							return out  # BxNx2048






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def __init__(			self						,					_a						,					_a						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   [json.loads(_a					) for l in open(_a					)]
							lowerCamelCase      =   os.path.dirname(_a					)
							lowerCamelCase      =   tokenizer
							lowerCamelCase      =   labels
							lowerCamelCase      =   len(_a					)
							lowerCamelCase      =   max_seq_length

							lowerCamelCase      =   transforms





				def __len__(			self					):





							"""simple docstring"""
							return len(self.data					)





				def __getitem__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]						,					add_special_tokens=_a					)					)
							lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   sentence[0], sentence[1:-1], sentence[-1]
							lowerCamelCase      =   sentence[: self.max_seq_length]

							lowerCamelCase      =   torch.zeros(self.n_classes					)
							lowerCamelCase      =   1

							lowerCamelCase      =   Image.open(os.path.join(self.data_dir						,					self.data[index]["""img"""]					)					).convert("""RGB"""					)
							lowerCamelCase      =   self.transforms(_a					)

							return {
							    "image_start_token": start_token,
							    "image_end_token": end_token,
							    "sentence": sentence,
							    "image": image,
							    "label": label,
							}





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   Counter()
							for row in self.data:
										label_freqs.update(row["""label"""]					)
							return label_freqs




def        a__					(							snake_case__							)		->   Optional[int]:
			lowerCamelCase      =   [len(row["""sentence"""]							) for row in batch]
			lowerCamelCase ,  lowerCamelCase      =   len(snake_case__							), max(snake_case__							)

			lowerCamelCase      =   torch.zeros(snake_case__			,					snake_case__			,					dtype=torch.long							)
			lowerCamelCase      =   torch.zeros(snake_case__			,					snake_case__			,					dtype=torch.long							)

			for i_batch, (input_row, length) in enumerate(zip(snake_case__			,					snake_case__							)							):
						lowerCamelCase      =   input_row["""sentence"""]
						lowerCamelCase      =   1

			lowerCamelCase      =   torch.stack([row["""image"""] for row in batch]							)
			lowerCamelCase      =   torch.stack([row["""label"""] for row in batch]							)
			lowerCamelCase      =   torch.stack([row["""image_start_token"""] for row in batch]							)
			lowerCamelCase      =   torch.stack([row["""image_end_token"""] for row in batch]							)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor



def        a__					(							)		->   str:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]



def        a__					(							)		->   List[str]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_56							),
			        transforms.CenterCrop(2_24							),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]			,					std=[0.1222_1994, 0.1214_5835, 0.1438_0469]			,					),
			    ]							)









"""simple docstring"""




from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase				:     Union[str, Any]					=				logging.get_logger(__name__)

lowerCAmelCase				:     Union[str, Any]					=				{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"mctct"





				def __init__(			self						,					_a=8_065						,					_a=1_536						,					_a=36						,					_a=6_144						,					_a=4						,					_a=384						,					_a=920						,					_a=1e-5						,					_a=0.3						,					_a="relu"						,					_a=0.02						,					_a=0.3						,					_a=0.3						,					_a=1						,					_a=0						,					_a=2						,					_a=1						,					_a=0.3						,					_a=1						,					_a=(7,)						,					_a=(3,)						,					_a=80						,					_a=1						,					_a=None						,					_a="sum"						,					_a=False						,					**_a						,					):





							"""simple docstring"""
							super().__init__(**_a						,					pad_token_id=_a						,					bos_token_id=_a						,					eos_token_id=_a					)
							lowerCamelCase      =   vocab_size
							lowerCamelCase      =   hidden_size
							lowerCamelCase      =   num_hidden_layers
							lowerCamelCase      =   intermediate_size
							lowerCamelCase      =   num_attention_heads
							lowerCamelCase      =   attention_head_dim
							lowerCamelCase      =   max_position_embeddings
							lowerCamelCase      =   layer_norm_eps
							lowerCamelCase      =   layerdrop
							lowerCamelCase      =   hidden_act
							lowerCamelCase      =   initializer_range
							lowerCamelCase      =   hidden_dropout_prob
							lowerCamelCase      =   attention_probs_dropout_prob
							lowerCamelCase      =   pad_token_id
							lowerCamelCase      =   bos_token_id
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   conv_glu_dim
							lowerCamelCase      =   conv_dropout
							lowerCamelCase      =   num_conv_layers
							lowerCamelCase      =   input_feat_per_channel
							lowerCamelCase      =   input_channels
							lowerCamelCase      =   conv_channels
							lowerCamelCase      =   ctc_loss_reduction
							lowerCamelCase      =   ctc_zero_infinity

							# prevents config testing fail with exporting to json
							lowerCamelCase      =   list(_a					)
							lowerCamelCase      =   list(_a					)

							if len(self.conv_kernel					) != self.num_conv_layers:
										raise ValueError(
										    """Configuration for convolutional module is incorrect. """
										    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
										    f'but is `len(config.conv_kernel) = {len(self.conv_kernel					)}`, '
										    f'`config.num_conv_layers = {self.num_conv_layers}`.'					)



